Automatic detection and segmentation of offset lithography defects using full reference image comparison
Madhawa, Dedigama (2017)
Diplomityö
Madhawa, Dedigama
2017
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2017122856125
https://urn.fi/URN:NBN:fi-fe2017122856125
Tiivistelmä
Offset printing has become the modern standard in high-volume industrial printing of magazines
and newspapers. Therefore, the continuous improvement of the printing quality through the
detection and removal of printing defects is an indispensable necessity. A vision-based inspection
method that compares an inspected image to a reference image is proposed to detect and segment
printing defects. The structural similarity image metric is used together with a Bayesian Classifier
to detect defective regions in the inspected image. The methods are evaluated using a dataset of
artificially simulated images based on defects samples from the printing facility. Results show that
the proposed methods can detect 93% of the simulated defects.
and newspapers. Therefore, the continuous improvement of the printing quality through the
detection and removal of printing defects is an indispensable necessity. A vision-based inspection
method that compares an inspected image to a reference image is proposed to detect and segment
printing defects. The structural similarity image metric is used together with a Bayesian Classifier
to detect defective regions in the inspected image. The methods are evaluated using a dataset of
artificially simulated images based on defects samples from the printing facility. Results show that
the proposed methods can detect 93% of the simulated defects.